Same Mask, Conflicting Contexts: Learning to Localize AI Edits

Knowing that an image has been edited does not specify where it was edited. We study how this distinction should shape the adaptation of spatial visual encoders. Opposite-status context training (OSCT) keeps the recipient's mask fixed while changing the image that supplies attention: in an auxiliary forward, edited recipients read keys and values from unedited donors, and vice versa, with donors drawn from different parent images. Natural and altered-context forwards jointly train the same PE-Spatial localizer; deployment remains an ordinary single-image forward. On a 37,162-image modern-edit evaluation, five 384-update continuations from one shared supervised checkpoint improve edited-image macro F1 by 1.32 points and pixel AP by 1.31 points over matched ordinary training, while reducing pristine-image false-positive area from 0.54% to 0.41%. OSCT also improves F1, IoU, and AP on three external localization sets. Parent-matched donor comparisons support opposite-status selection over same-status exchange, and the improvement persists after matched score refitting of both trained endpoints. These results identify the relationship between borrowed context and recipient supervision as a useful training choice for AI-edit localization, without adding an inference module.